How Good is Your Model (Angry Birds) Part 2 - Refining my process

A year ago, I wrote about my attempt to integrate Angry Birds as part of my quadratic modeling unit. I was certainly not the first, and there have been many others that have taken this idea and run with it. This is definitely a great way of using the concept of fitting parabolas to a realistic task that the students can have fun completing.

As I said a year ago, however, the bigger picture skill that is really powerful with modeling is making do with less information. I incentivized my students last year to come up with a model that predicts the final location of the collision of a bird earlier than everyone else. In other words, if Thomas is able to predict the correct final location with ten seconds of data, while Nick is able to do so with only seven, Nick has done the better job of modeling. I did this by asking the students to try to do this with the earliest possible frame in the video.

This time, I have found a better way to do this. Five videos, all of them cut short.
I'm asking the students to complete this table:
Screen Shot 2013-01-22 at 6.03.12 PM

The impact ratio is defined as the ratio of the orange line to the yellow line, as shown in this image:
Screen Shot 2013-01-22 at 6.04.44 PM

Each group of students will calculate the ratio for each video using Geogebra. Some videos reveal more about the path than others. I'll sum the errors, rank the student groups based on cumulative error, and then we'll have a great discussion about what made this difficult.

The sensitivity of a quadratic (or any fit) fit to data points that are close together is what I'm targeting here. I've tried other techniques to flesh this out in students before - I still get students 'fitting' a table of data by choosing the first two or three points. I'm hoping this will be a bit more interesting and successful than my previous attempts.

Trimmed Angry Bird Videos:





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